Multi-drug chemotherapy has been shown to be a more effective treatment than with a single drug, and research efforts have endeavored to develop methods and models for selecting optimal drug combinations, doses, and schedules likely to be most effective and not excessively toxic. However, heterogeneity in clinical response due to variations in tumor cell structure and cell cycle kinetics remains a formidable challenge, especially for developing optimal treatment regimens among individuals. The many factors that affect responses to chemotherapy, such as rates of cell division, cell death, and the evolution of drug resistance, among others, interact in complex ways. Both biochemical and kinetic resistance affect treatment outcomes (Norton & Simon, 1986). In addition, there is extreme inter-patient and intra-patient heterogeneity in these factors. The evolution of resistance in particular confounds therapeutic attempts and stands as one of the primary obstacles to successful treatment. The speed at which resistance evolves depends on the rates of mutation, division, and death of tumor cells. In addition, treatment efficacy and evolution of resistance depends on the types of drugs and the doses and schedules by which they are applied.
Notwithstanding these and other complexities, individually tailored treatment regimens, and the methods, systems and models for producing such regimens, are beginning to receive greater attention as more information becomes available about specific genes involved in oncogenesis and drug responses, and as methods to measure cell kinetics become feasible to perform in a clinical setting. For example, U.S. patent application Publication No. U.S. 2002/0095258 A1 discloses, in one embodiment, a method and system for recommending optimal treatment protocols for an individual. In another embodiment, a method and system for predicting progression of a biological process in an individual patient under a plurality of protocols is also disclosed. The system model is based on discrete time and requires an unspecified “fitness function” to optimize treatments. And while this publication suggests incorporating data from the clinic (tumor biopsies) in the selection of parameters, the required parameters remains unspecified other than a need to know all the age-specific and time-specific transition probabilities of cells between every phase of the cell cycle, and how each of these probabilities is affected by treatment. And many key limiting factors necessary for realistic biological modeling, such as evolution of drug resistance in response to treatment, and the rate of apoptosis, among others, are not incorporated into the model.
There is therefore a need for a more realistic model and effective method and system for modeling biological systems using tumor cell kinetics and incorporating evolution to predict response to treatment in realistic circumstances. Such tumor cell kinetics would be based on parameters measured from tumor biopsies (e.g. apoptotic index, proliferative fraction, S-phase fraction, cell-cycle time, drug resistance), and thereby enable tailoring of treatment to individuals. Such a model would reduce the number of clinical trials to be performed, as resources could be directed to trials predicted by the model to result in a statistically significant improvement in outcome. Additionally, more robust predictions may be produced before treatments are tried in vivo.